Research on Human Pose Estimation Based on High-resolution Network

Abstract

The problem addressed in this paper is the increase in the parameter quantity of existing human body pose estimation network models when the depth of the network is scaled up to improve forecast precision. To tackle this issue, an optimization network model of the new human body pose estimation called BDENet is proposed, inspired by high-resolution detection networks. This model incorporates a bottleneck structure and dilated convolution to reduce parameters and incorporates the ECA lightweight attention mechanism to enhance precision. Compared with HRNet, the proposed model achieves a 21.4% reduction in parameter quantity on the MSCOCO dataset while scaling up precision by 1.4%. The experimental findings strongly suggest that the modified network significantly improves network precision along with lowering the number of parameters.